894 resultados para Artificial tooth


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The microstructure of an artificial grain boundary in an YBa2Cu3O7-δ (YBCO) thin film grown on a (100)(110), [001]-tilt yttria-stabilized-zirconia (YSZ) bicrystal substrate has been studied using transmission electron microscopy (TEM). The orientation relationship between the YBCO film and the YSZ substrate was [001]YBCO∥[001]YSZ and [110]YBCO∥[100]YSZ for each half of the bicrystal film. However, the exact boundary geometry of the bicrystal substrate was not transferred to the film. The substrate boundary was straight while the film boundary was wavy. In several cases there was bending of the lattice confined within a distance of a few basal-plane lattice spacings from the boundary plane and microfaceting. No intergranular secondary phase was observed but about 25% of the boundary was covered by c-axis-tilted YBCO grains and a-axis-oriented grains, both of which were typically adjacent to CuO grains or surrounded by a thin Cu-rich amorphous layer.

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The microstructure of artificial grain boundaries in YBa2Cu3O7-δ (YBCO) thin films grown on [001] tilt YZrO2 (YSZ) bicrystal substrates has been characterized using transmission electron microscopy and atomic force microscopy. Despite a relatively straight morphology of the substrate boundaries, the film boundaries were wavy. The waviness was a result of the combined effects of grooving at the substrate boundaries prior to the film deposition and an island-growth mechanism for YBCO on YSZ substrates. The dihedral angle of the groove walls varied with the misorientation angle and depended on the symmetry of the substrate boundary. The amplitudes of the film boundary waviness compared well with the widths of the grooves. In addition, the grooves induced local bending of the YBCO lattice planes and additional tilt components perpendicular to the c-axis close to the film boundaries. © 1995.

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Weak links were fabricated by pulsed laser deposition of YBa 2Cu3Ox thin films on Y-ZrO2 bicrystal substrates. They were formed by transferring the bicrystal boundary into the epitaxial film during the film growth. Their properties were determined by the misorientation angle ( theta ) between the two halves of the bicrystal. The transport properties of the weak links were studied as a function of theta and an exponential dependence of the weak link critical current density was observed for angles up to 45 degrees . Clear Josephson effects with good microwave and magnetic field response were observed.

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We have studied weak links and dc-SQUIDs made from pulsed laser deposited YBa2Cu3O7-δ thin films on Y-ZrO 2 bicrystal substrates. The transport properties of the weak links were studied as a function of the misorientation angle (θ) between the two halves of the bicrystal and an exponential dependence of the weak link critical current density was observed for angles up to 40°at 77 K. Josephson effects with clear microwave and magnetic field responses were observed. An optimum dc-SQUID performance at 77 K was obtained for θ=32°. At this temperature, we achieved a periodic magnetic field response with a modulation depth of 12 μV.

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Engineered grain boundary Josephson junctions in YBaCuO were formed on bicrystal Y-ZrO2 substrates. Laser deposited films were patterned into micron size microbridges. The authors obsd. a pronounced correlation between superconducting transport properties of grain boundary junctions and the misorientation angle θ between the two halves of the bicrystal. The crit. Josephson current Ic decreased about four orders of magnitude as θ was increased from 0 to 45 degrees. Clear microwave and magnetic field responses were obsd. at 77 K. At this temp., crit. current times normal resistance products, IcRn, of up to 1 mV were measured for low angle grain boundaries, and Shapiro steps were obsd. up to that voltage. DC SQUIDs were fabricated, and best performance at 77 K was obtained for θ = 32° with a 4-μm strip width. To utilize the higher IcRn value of a lower θ, submicron junctions have to be developed. [on SciFinder(R)]

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Total Artificial Hearts are mechanical pumps which can be used to replace the failing natural heart. This novel study developed a means of controlling a new design of pump to reproduce physiological flow bringing closer the realisation of a practical artificial heart. Using a mathematical model of the device, an optimisation algorithm was used to determine the best configuration for the magnetic levitation system of the pump. The prototype device was constructed and tested in a mock circulation loop. A physiological controller was designed to replicate the Frank-Starling like balancing behaviour of the natural heart. The device and controller provided sufficient support for a human patient while also demonstrating good response to various physiological conditions and events. This novel work brings the design of a practical artificial heart closer to realisation.

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In plants, double-stranded RNA (dsRNA) is an effective trigger of RNA silencing, and several classes of endogenous small RNA (sRNA), processed from dsRNA substrates by DICER-like (DCL) endonucleases, are essential in controlling gene expression. One such sRNA class, the microRNAs (miRNAs) control the expression of closely related genes to regulate all aspects of plant development, including the determination of leaf shape, leaf polarity, flowering time, and floral identity. A single miRNA sRNA silencing signal is processed from a long precursor transcript of nonprotein-coding RNA, termed the primary miRNA (pri-miRNA). A region of the pri-miRNA is partially self-complementary allowing the transcript to fold back onto itself to form a stem-loop structure of imperfectly dsRNA. Artificial miRNA (amiRNA) technology uses endogenous pri-miRNAs, in which the miRNA and miRNA*(passenger strand of the miRNA duplex) sequences have been replaced with corresponding amiRNA/ amiRNA*sequences that direct highly efficient RNA silencing of the targeted gene. Here, we describe the rules for amiRNA design, as well as outline the PCR and bacterial cloning procedures involved in the construction of an amiRNA plant expression vector to control target gene expression in Arabidopsis thaliana. © 2014 Springer Science+Business Media New York.

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It is known that 22-nucleotide (nt) microRNAs (miRNAs) derived from asymmetric duplexes trigger phased small-interfering RNA (phasiRNA) production from complementary targets. Here we investigate the efficacy of 22-nt artificial miRNA (amiRNA)-mediated RNA silencing relative to conventional hairpin RNA (hpRNA) and 21-nt amiRNA-mediated RNA silencing. CHALCONE SYNTHASE (CHS) was selected as a target in Arabidopsis thaliana due to the obvious and non-lethal loss of anthocyanin accumulation upon widespread RNA silencing. Over-expression of CHS in the pap1-D background facilitated visual detection of both local and systemic RNA silencing. RNA silencing was initiated in leaf tissues from hpRNA and amiRNA plant expression vectors under the control of an Arabidopsis RuBisCo small subunit 1A promoter (SSU). In this system, hpRNA expression triggered CHS silencing in most leaf tissues but not in roots or seed coats. Similarly, 21-nt amiRNA expression from symmetric miRNA/miRNA* duplexes triggered CHS silencing in all leaf tissues but not in roots or seed coats. However, 22-nt amiRNA expression from an asymmetric duplex triggered CHS silencing in all tissues, including roots and seed coats, in the majority of plant lines. This widespread CHS silencing required RNA-DEPENDENT RNA POLYMERASE6-mediated accumulation of phasiRNAs from the endogenous CHS transcript. These results demonstrate the efficacy of asymmetric 22-nt amiRNA-directed RNA silencing and associated phasiRNA production and activity, in mediating widespread RNA silencing of an endogenous target gene. Asymmetric 22-nt amiRNA-directed RNA silencing requires little modification of existing amiRNA technology and is expected to be effective in suppressing other genes and/or members of gene families.

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In plant cells, DICER-LIKE4 processes perfectly double-stranded RNA (dsRNA) into short interfering (si) RNAs, and DICER-LIKE1 generates micro (mi) RNAs from primary miRNA transcripts (pri-miRNA) that form fold-back structures of imperfectly dsRNA. Both si and miRNAs direct the endogenous endonuclease, ARGONAUTE1 to cleave complementary target single-stranded RNAs and either small RNA (sRNA)-directed pathway can be harnessed to silence genes in plants. A routine way of inducing and directing RNA silencing by siRNAs is to express self-complementary single-stranded hairpin RNA (hpRNA), in which the duplexed region has the same sequence as part of the target gene's mRNA. Artificial miRNA (amiRNA)-mediated silencing uses an endogenous pri-miRNA, in which the original miRNA/miRNA* sequence has been replaced with a sequence complementary to the new target gene. In this chapter, we describe the plasmid vector systems routinely used by our research group for the generation of either hpRNA-derived siRNAs or amiRNAs.

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We report here that the expression of endogenous microRNAs (miRNAs) can be efficiently silenced in Arabidopsis thaliana (Arabidopsis) using artificial miRNA (amiRNA) technology. We demonstrate that an amiRNA designed to target a mature miRNA directs silencing against all miRNA family members, whereas an amiRNA designed to target the stem-loop region of a miRNA precursor transcript directs silencing against only the individual family member targeted. Furthermore, our results indicate that amiRNAs targeting both the mature miRNA and stem-loop sequence direct RNA silencing through cleavage of the miRNA precursor transcript, which presumably occurs in the nucleus of a plant cell during the initial stages of miRNA biogenesis. This suggests that small RNA (sRNA)-guided RNA cleavage in plants occurs not only in the cytoplasm, but also in the nucleus. Many plant miRNA gene families have been identified via sequencing and bioinformatic analysis, but, to date, only a small tranche of these have been functionally characterized due to a lack of effective forward or reverse genetic tools. Our findings therefore provide a new and powerful reverse-genetic tool for the analysis of miRNA function in plants. © The Author 2010. Published by the Molecular Plant Shanghai Editorial Office in association with Oxford University Press on behalf of CSPP and IPPE, SIBS, CAS.

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The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.

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Purpose of review: Artificial corneas are being developed to meet a shortage of donor corneas as well as to address cases where allografting is contraindicated. A range of artificial corneas has been developed. Here we review several newer designs and especially those inspired by naturally occurring biomaterials found with the human body and elsewhere. Recent findings: Recent trends in the development of artificial corneas indicate a move towards the use of materials derived from native sources including decellularized corneal tissue and tissue substitutes synthesized by corneal cells in vitro when grown either on their own, or in conjunction with novel protein-based scaffolds. Biologically inspired materials are also being considered for implantation on their own with the view to promoting endogenous corneal tissue. Summary: More recent attempts at making artificial corneas have taken a more nature-based or nature-inspired approach. Several will in the near future be likely to be available clinically.

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An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

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The design and synthesis of molecularly or supramolecularly defined interfacial architectures have seen in recent years a remarkable growth of interest and scientific research activities for various reasons. On the one hand, it is generally believed that the construction of an interactive interface between the living world of cells, tissue, or whole organisms and the (inorganic or organic) materials world of technical devices such as implants or medical parts requires proper construction and structural (and functional) control of this organism–machine interface. It is still the very beginning of generating a better understanding of what is needed to make an organism tolerate implants, to guarantee bidirectional communication between microelectronic devices and living tissue, or to simply construct interactive biocompatibility of surfaces in general. This exhaustive book lucidly describes the design, synthesis, assembly and characterization, and bio-(medical) applications of interfacial layers on solid substrates with molecularly or supramolecularly controlled architectures. Experts in the field share their contributions that have been developed in recent years.

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Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.